Smart fire detection, a critical part of monitoring systems, aims at providing safety and robustness in Industry 4.0. We analyzed a
fire detection application developed by Dunnings and Breckon [1]} using convolutional neural network (CNN). It automatically detects fire regions (pixels) in the frames of a surveilled video in a real-time manner. Among other implementations, we deploy the FireNet model that accurately identifies and locate fire in each frame of a given video segment. FireNet is a lightweight variation of AlexNet model [2]} with three convolutional layers of sizes 64, 128, and 256. In this model, each convolutional layer is augmented by a max-pooling layer and a local response normalization to achieve high frequency features with a large response from previous layer. To analyze the inference time of the fire detection system, we constructed a benchmarking dataset of 240 videos with different backgrounds. For fair and realistic analysis, the length of all videos is considered two seconds.
